fal-ai/ltx-2.3-quality/text-to-audio/lora

Text to Audio high-quality using LTX-2.3 with Lora

Inference

Commercial use

Schema

LLMs

Playground API Examples

About

Text-to-audio with up to 3 user-supplied LoRAs.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Please check the migration guide for more information.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/ltx-2.3-quality/text-to-audio/lora", {
  input: {
    prompt: "A woman says: 'Oh, what a lovely day we are having!' Natural speech, clean studio audio.",
    loras: [{
      path: "https://example.com/path/to/lora.safetensors",
      scale: 1,
      transformer: "both"
    }]
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fal-ai/ltx-2.3-quality/text-to-audio/lora", {
  input: {
    prompt: "A woman says: 'Oh, what a lovely day we are having!' Natural speech, clean studio audio.",
    loras: [{
      path: "https://example.com/path/to/lora.safetensors",
      scale: 1,
      transformer: "both"
    }]
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("fal-ai/ltx-2.3-quality/text-to-audio/lora", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("fal-ai/ltx-2.3-quality/text-to-audio/lora", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

prompt string* required

The prompt to generate the audio from.

num_frames integer

The audio duration expressed in LTX video-frame units. Duration is approximately num_frames / frames_per_second seconds. Default value: 121

frames_per_second float

Frame rate used to derive the generated audio duration. Default value: 24

num_inference_steps integer

Number of inference steps. Default value: 15

guidance_scale float

Classifier-free guidance scale. Default value: 1

negative_prompt string

The negative prompt to steer generation away from. Default value: "pc game, console game, video game, cartoon, childish, ugly"

seed integer

Random seed for reproducibility. If None, a random seed is chosen.

enable_prompt_expansion boolean

Whether to enable prompt expansion.

enable_safety_checker boolean

Whether to enable the safety checker. Default value: true

sync_mode boolean

If True, the audio is returned as a data URI inline in the response. Useful for short-lived requests and tests.

loras list<LoRAInput>* required

Up to 3 LoRAs to apply on top of LTX-2.3. Each path is downloaded through the registry SSRF-safe downloader before it is loaded. Max size: 3 GB per LoRA.

{
  "prompt": "A woman says: 'Oh, what a lovely day we are having!' Natural speech, clean studio audio.",
  "num_frames": 121,
  "frames_per_second": 24,
  "num_inference_steps": 15,
  "guidance_scale": 1,
  "negative_prompt": "pc game, console game, video game, cartoon, childish, ugly",
  "enable_safety_checker": true,
  "loras": [
    {
      "path": "https://example.com/path/to/lora.safetensors",
      "scale": 1,
      "transformer": "both"
    }
  ]
}

Output#

audio File* required

The generated audio.

seed integer* required

The seed actually used for generation.

prompt string* required

The prompt used for generation (after any expansion).

{
  "audio": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  },
  "prompt": ""
}

Other types#

VideoFile#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

width integer

The width of the video

height integer

The height of the video

fps float

The FPS of the video

duration float

The duration of the video

num_frames integer

The number of frames in the video

ImageSize#

width integer

The width of the generated image. Default value: 512

height integer

The height of the generated image. Default value: 512

File#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

fal-ai/ltx-2.3-quality/text-to-audio/lora

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

VideoFile#

ImageSize#

File#

Related Models